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All Journal PROtek : Jurnal Ilmiah Teknik Elektro Rang Teknik Journal
Yamashika, Herris
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering

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PENGUJIAN KARATERISTIK PENGISIAN BATERAI DARI GENERATOR DC MAGNET PERMANEN MENGGUNAKAN SOLAR CHARGING CONTROLLER Yamashika, Herris; Kamil, Mahyessie
Rang Teknik Journal Vol 4, No 1 (2021): Vol. 4 No. 1 Januari 2021
Publisher : Fakultas Teknik Universitas Muhammadiyah Sumatera Barat

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (195.408 KB) | DOI: 10.31869/rtj.v4i1.2280

Abstract

Penelitian ini melakukan pengujian karateristik pengisian baterai yang menggunakan generator DC magnet permanen sebagai sumber, dan pengaturan pengisian baterai menggunakan Solar Charging Controller (SCC) yang sering dipakai sebagai pengatur pengisian dari panel surya. Pengujian ini dilakukan untuk mengetahui nilai tegangan dan rpm minimal generator agar baterai dapat terisi. Pelaksanaan pengujian dilakukan menggunakan generator DC magnet permanen dengan kapasitas 250 watt, 24 Volt, pada putaran 2750 rpm. Genarator ini dihubungkan ke SCC untuk mengatur pengisian baterai VRLA 12 Volt 7 Ah, dan beban lampu 12 Volt 5 Watt sebanyak 20 buah. Sebagai penggerak mula, digunakan motor induksi 3 fasa yang putarannya dapat diatur melalui Variable Speed Drive (VSD). Parameter arus dan tegangan dari masing-masing generator, baterai, dan beban lampu akan diamati. Untuk mengetahui putarang generator, rpm meter digunakan untuk mengetahui putaran generator. Dari hasil pengujian diperoleh bahwa tegangan minimum generator agar dapat mengisi baterai adalah 12.3 Volt pada putaran 1630 rpm.
A Novel Model for Prediction of Flashover 150kV Polluted Insulator Based on Nonlinear Autoregressive External Input Neural Network Hasanah, Mardini; Novizon, Novizon; Qatrunnada, Rusvaira; Warmi, Yusreni; Amalia, Sitti; Yamashika, Herris
PROtek : Jurnal Ilmiah Teknik Elektro Vol 11, No 3 (2024): Protek: Jurnal Ilmiah Teknik Elektro
Publisher : Program Studi Teknik Elektro Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/protk.v11i3.7344

Abstract

This study aims to use an artificial neural network to forecast the flashover voltage of a polluted high-voltage insulator. Practical tests were conducted on a high-voltage insulator to gather data for the neural network. These tests were carried out with varying levels of real contaminants from used insulators, with each level of contamination measured in milliliters. The collected data provides flashover voltage values corresponding to different pollution amounts and their conductivity in each insulator zone. The Nonlinear Autoregressive External Input Neural Network (NarxNet) is employed to predict the flashover voltage and assess the pollution state of the insulator. The results demonstrate that the NarxNet method achieves a 93.74% accuracy rate in predicting the flashover voltage of high-voltage insulators, compared to the results from practical tests.
A Novel Model for Prediction of Flashover 150kV Polluted Insulator Based on Nonlinear Autoregressive External Input Neural Network Hasanah, Mardini; Novizon, Novizon; Qatrunnada, Rusvaira; Warmi, Yusreni; Amalia, Sitti; Yamashika, Herris
PROtek : Jurnal Ilmiah Teknik Elektro Vol 11, No 3 (2024): Protek: Jurnal Ilmiah Teknik Elektro
Publisher : Program Studi Teknik Elektro Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/protk.v11i3.7344

Abstract

This study aims to use an artificial neural network to forecast the flashover voltage of a polluted high-voltage insulator. Practical tests were conducted on a high-voltage insulator to gather data for the neural network. These tests were carried out with varying levels of real contaminants from used insulators, with each level of contamination measured in milliliters. The collected data provides flashover voltage values corresponding to different pollution amounts and their conductivity in each insulator zone. The Nonlinear Autoregressive External Input Neural Network (NarxNet) is employed to predict the flashover voltage and assess the pollution state of the insulator. The results demonstrate that the NarxNet method achieves a 93.74% accuracy rate in predicting the flashover voltage of high-voltage insulators, compared to the results from practical tests.
Co-Authors Hasanah, Mardini Kamil, Mahyessie Novizon, Novizon Qatrunnada, Rusvaira Sitti Amalia Yusreni Warmi